Learning Audio-Text Agreement for Open-vocabulary Keyword Spotting

International Conference
2022-06-16 18:27
Authors : Hyeon-Kyeong Shin, Hyewon Han, Doyeon Kim, Soo-Whan Chung, Hong-Goo Kang

Year : 2022

Publisher / Conference : INTERSPEECH (*Best Student Paper Finalist)

Research area : Speech Signal Processing, Keyword Spotting, Multi-modal Signal Processing

Presentation/Publication date : 2022.09.20

Related project : 음성인식 성능 향상을 위한 원단 신호 전처리 및 키워드 인식 알고리즘 개발

Presentation : Oral

In this paper, we propose a novel end-to-end user-defined keyword spotting method that utilizes linguistically corresponding patterns between speech and text sequences. Unlike previous approaches requiring speech keyword enrollment, our method compares input queries with an enrolled text keyword sequence. To place the audio and text representations within a common latent space, we adopt an attention-based cross-modal matching approach that is trained in an end-to-end manner with monotonic matching loss and keyword classification loss. We also utilize a de-noising loss for the acoustic embedding network to improve robustness in noisy environments. Additionally, we introduce the LibriPhrase dataset, a new short-phrase dataset based on LibriSpeech for efficiently training keyword spotting models. Our proposed method achieves competitive results on various evaluation sets compared to other single-modal and cross-modal baselines.
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